MétaCan
Menu
Back to cohort
Record W4406003794 · doi:10.15353/juhr.v1i3.6105

A Gendered Perspective of the Lack of Justice for the Asubpeeschoseewagoon Anishinabek Women of Grassy Narrows

2024· article· en· W4406003794 on OpenAlexaffabout
Amanda Armstrong

Bibliographic record

VenueUniversity of Waterloo Journal of Undergraduate Health Research · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicEnvironmental Justice and Health Disparities
Canadian institutionsUniversity of Waterloo
FundersAustralian Government
KeywordsSustenanceEnvironmental justiceLivelihoodEmpowermentDamagesEconomic JusticeGovernment (linguistics)SocioeconomicsSociologyPolitical scienceBusinessGeographyAgricultureLaw

Abstract

fetched live from OpenAlex

From 1962 to 1970, the Reed Paper Mill dumped over 9000 kilograms of mercury into the English-Wabigoon River system in Northern Ontario. The Grassy Narrows community, an Anishinabek First Nation, depend on this water system for sustenance and their livelihoods. Their reliance specifically on fish as a primary source of food and income increases their community’s vulnerability to mercury poisoning, which has resulted in lasting impacts on the physical and mental health of their community. Grassy Narrows women are disproportionately impacted by mercury poisoning due to their gendered responsibility to protect water, care work, and child-bearing. This paper examines the failure of the Government of Ontario to uphold the foundational elements of environmental justice for Grassy Narrows women, including distributive fairness, procedural fairness, and corrective action. Recommendations for the Government of Ontario include listening to the needs of Grassy Narrows First Nations and including them in decision-making processes to help ease the generational pain caused by perpetual environmental racism.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.220
Threshold uncertainty score0.443

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0310.037
Scholarly communication0.0060.003
Open science0.0010.004
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.183
GPT teacher head0.422
Teacher spread0.239 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueUniversity of Waterloo Journal of Undergraduate Health ResearchSame topicEnvironmental Justice and Health DisparitiesFrench-language works237,207